Instrumental activities of daily living (IADL) evaluation system based on EEG signal feature analysis

This work proposes an IADL evaluation system using LDA algorithm based on EEG signal, which explores the correlation between the subjective IADL assessment and the objective EEG signals measurement. Five features are extracted from the single channel EEG device including average amplitude, power ratio, spectral central, spectral edge frequency 25% and 50%. These features are represented as an indicator of participant's IADL and are classified as IADL scales using LDA algorithm. For system evaluation, thirty elderly participants (70 ~ 96 years old) are classified into three groups by IADL score: high (disability-free, 16~24 points), medium (mild disability, 8 ~ 15 points) and low (severe disability, 0 ~ 7 points). These IADL groups distribute uniformly to conduct following IADL scenarios; 1. Ability to use telephone, 2. Ability to handle finances, and 3. Chat with people (that is not included in IADL scenario). The experiment result shows that the proposed EEG features and evaluation system can achieve 90% average accuracy rate verified by Leave-One-Out cross validation (LOOCV).